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BayesRGMM: Bayesian Robust Generalized Mixed Models for Longitudinal Data
To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.
| Version: |
2.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp (≥ 1.0.1), MASS, batchmeans, abind, reshape, msm, mvtnorm, plyr, Rdpack |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppDist |
| Suggests: |
testthat |
| Published: |
2022-05-10 |
| DOI: |
10.32614/CRAN.package.BayesRGMM |
| Author: |
Kuo-Jung Lee 
[aut, cre],
Hsing-Ming Chang [ctb],
Ray-Bing Chen [ctb],
Keunbaik Lee [ctb],
Chanmin Kim [ctb] |
| Maintainer: |
Kuo-Jung Lee <kuojunglee at ncku.edu.tw> |
| License: |
GPL-2 |
| URL: |
https://sites.google.com/view/kuojunglee/r-packages |
| NeedsCompilation: |
yes |
| CRAN checks: |
BayesRGMM results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BayesRGMM
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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